In this article, I’ll walk you through the process of creating a complete end-to-end solution using Pinecone, OpenAI and Llama-Index.
Before we deep dive into it, here is the quick overview of each of these components:
Pinecone is a cloud-native vector database designed for storing and querying high-dimensional vectors. It provides fast and efficient search over vector embeddings. It has a simple API and no infrastructure hassles. It is one of the best solutions for those who are looking for query results with low latency at the scale of billions of vectors.
OpenAI models can be used for generating the embeddings as well as for text completions. By combining OpenAI’s models with Pinecone, we can achieve deep learning capabilities for embedding generations along with efficient vector storage and retrieval.
Llama-Index is a framework that enables developers to integrate diverse data sources with LLMs like OpenAI and also provides tools to augment LLM applications with data.
Now, we have the high-level idea of all the foundational parts, let’s do a deep dive into the implementation.
Firstly, we need to grab the OpenAI key as shown below:
Get An OpenAI API Key
To get the OpenAI key, you need to go to https://openai.com/, login and then grab the keys using highlighted way:
Once you have the key, do set it in an environment variable. Below is the code to do this:
os.environ["OPENAI_API_KEY"] = "PASTE_YOUR_KEY_HERE"
Preparing The Data
Next, we need to load our data into the Pandas data frame. You can have your own data sources and not the same as mine. I’ve grabbed my data from Hugging Face, which contains contextual information along with questions and answers.